The current social media boom has made it easy for individuals to publically describe, publish and disseminate their experiences with conducting business with specific organizations to a large audience. Today, a large number of organizations attempt to tap into such customer feedback to understand problem areas faced by their customers, and to use such feedback to make improvements and corrections.
In order to meaningfully analyze the potentially large volume of customer feedback that a business may collect, a typical approach may predefine topics/themes relevant to a specific business function and then develop an approach to map specific customer feedback to an appropriate theme. Typical approaches to mapping feedback to themes are mapping based on rule based patterns or using machine learning techniques.
Once mapped to themes, feedback may then be quantified and analyzed based on the volume associated with a discussion theme. However the use of a predefined set of themes to analyze unstructured data is inherently limiting—previously unseen problems may never get captured by a predefined template of themes and a lot of valuable feedback could get overlooked.
The ability for a business to automatically detect topics of discussion amongst their customers would considerably accelerate the ability of the business to respond to problems. Improved responsiveness would likely improve overall customer satisfaction which in turn would drive greater customer retention and profitability.
Human language is very complex and the authors of documents can choose to describe the same theme in many different ways which makes automatic identification of significant themes a very hard task. However, previous attempts at employing unsupervised techniques have so-far provided limited business value as cluster groupings are generally unintuitive to human interpretation.
Various embodiments includes systems and methods for automatic unsupervised detection of discussion topics from unstructured feedback text wherein the results of topic groupings are tagged with meaningful labels.